Book contents
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- 1 Neural networks for perceptual processing: from simulation tools to theories
- 2 Sensory ecology and perceptual allocation: new prospects for neural networks
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
1 - Neural networks for perceptual processing: from simulation tools to theories
from Part I - General themes
Published online by Cambridge University Press: 05 July 2011
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- 1 Neural networks for perceptual processing: from simulation tools to theories
- 2 Sensory ecology and perceptual allocation: new prospects for neural networks
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
Summary
Introduction
This paper has two main aims. First, to give an introduction to some of the construction techniques – the ‘nuts-and-bolts’ as it were – of neural networks deployed by the authors in this book. Our intention is to emphasise conceptual principles and their associated terminology, and to do this wherever possible without recourse to detailed mathematical descriptions. However, the term ‘neural network’ has taken on a multitude of meanings over the last couple of decades, depending on its methodological and scientific context. A second aim, therefore, given that the application of the techniques described in this book may appear rather diverse, is to supply some meta-theoretical landmarks to help understand the significance of the ensuing results.
In general terms, neural networks are tools for building models of systems that are characterised by data sets which are often (but not always) derived by sampling a system input-output behaviour. While a neural network model is of some utility if it mimics the behaviour of the target system, it is far more useful if key mechanisms underlying the model functionality can be unearthed, and identified with those of the underlying system. That is, the modeller can ‘break into’ the model, viewed initially as an input-output ‘black box’, and find internal representations, variable relationships, and structures which may correspond with the underlying target system. This target system may be entirely non-biological (e.g. stock market prices), or be of biological origin, but have nothing to do with brains (e.g. ecologically driven patterns of population dynamics).
- Type
- Chapter
- Information
- Modelling Perception with Artificial Neural Networks , pp. 7 - 34Publisher: Cambridge University PressPrint publication year: 2010